def test_normalization():
""" Test the normalization routine applied after a convolution"""
# create kernel
D33 = 1.0
D44 = 0.04
t = 1
num_orientations = 5
k = EnhancementKernel(D33,
D44,
t,
orientations=num_orientations,
force_recompute=True)
# create a constant dataset
numorientations = k.get_orientations().shape[0]
spike = np.ones((7, 7, 7, numorientations), dtype=np.float64)
# convert dataset to SH
spike_sh = sf_to_sh(spike, k.get_sphere(), sh_order=8)
# convolve kernel with delta spike and apply normalization
csd_enh = convolve(spike_sh, k, sh_order=8, test_mode=True, normalize=True)
# convert dataset to DSF
csd_enh_dsf = sh_to_sf(csd_enh,
k.get_sphere(),
sh_order=8,
basis_type=None)
# test if the normalization is performed correctly
npt.assert_almost_equal(np.amax(csd_enh_dsf), np.amax(spike))
def test_normalization():
""" Test the normalization routine applied after a convolution"""
# create kernel
D33 = 1.0
D44 = 0.04
t = 1
num_orientations = 5
k = EnhancementKernel(D33, D44, t, orientations=num_orientations, force_recompute=True)
# create a constant dataset
numorientations = k.get_orientations().shape[0]
spike = np.ones((7, 7, 7, numorientations), dtype=np.float64)
# convert dataset to SH
spike_sh = sf_to_sh(spike, k.get_sphere(), sh_order=8)
# convolve kernel with delta spike and apply normalization
csd_enh = convolve(spike_sh, k, sh_order=8, test_mode=True, normalize=True)
# convert dataset to DSF
csd_enh_dsf = sh_to_sf(csd_enh, k.get_sphere(), sh_order=8, basis_type=None)
# test if the normalization is performed correctly
npt.assert_almost_equal(np.amax(csd_enh_dsf), np.amax(spike))
D33 = 1.0
D44 = 0.02
t = 1
k = EnhancementKernel(D33, D44, t)
"""
Visualize the kernel
"""
from dipy.viz import window, actor
from dipy.reconst.shm import sf_to_sh, sh_to_sf
scene = window.Scene()
# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(sf_to_sh(spike, k.get_sphere(), sh_order=8),
k,
sh_order=8,
test_mode=True)
spike_sf_conv = sh_to_sf(spike_shm_conv, default_sphere, sh_order=8)
model_kernel = actor.odf_slicer(spike_sf_conv * 6,
sphere=default_sphere,
norm=False,
scale=0.4)
model_kernel.display(x=3)
scene.add(model_kernel)
scene.set_camera(position=(30, 0, 0), focal_point=(0, 0, 0), view_up=(0, 0, 1))
window.record(scene, out_path='kernel.png', size=(900, 900))
if interactive:
window.show(scene)
t = 1
k = EnhancementKernel(D33, D44, t)
"""
Visualize the kernel
"""
from dipy.viz import fvtk
from dipy.data import get_sphere
from dipy.reconst.shm import sf_to_sh, sh_to_sf
ren = fvtk.ren()
# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(sf_to_sh(spike, k.get_sphere(), sh_order=8), k,
sh_order=8, test_mode=True)
sphere = get_sphere('symmetric724')
spike_sf_conv = sh_to_sf(spike_shm_conv, sphere, sh_order=8)
model_kernel = fvtk.sphere_funcs((spike_sf_conv * 6)[3,:,:,:],
sphere,
norm=False,
radial_scale=True)
fvtk.add(ren, model_kernel)
fvtk.camera(ren, pos=(30, 0, 0), focal=(0, 0, 0), viewup=(0, 0, 1), verbose=False)
fvtk.record(ren, out_path='kernel.png', size=(900, 900))
"""
.. figure:: kernel.png
:align: center